Diagnostic Significance of Serum IgG Galactosylation in CA19-9-Negative Pancreatic Carcinoma Patients

N-glycosylation in non-invasive and invasive intraductal papillary mucinous neoplasm

Intraductal papillary mucinous neoplasms (IPMNs), often found incidentally, are potentially malignant cystic tumors of the pancreas. Due to the precancerous nature, IPMNs lacking malignant features should be kept on surveillance. The follow-up relies on magnetic resonance imaging, which has a limited accuracy to define the high-risk patients. New diagnostic methods are thus needed to recognize IPMNs with malignant potential. Here, aberrantly expressed glycans constitute a promising new area of research. We compared the N-glycan profiles of non-invasive IPMN tissues (n = 10) and invasive IPMN tissues (n = 10) to those of non-neoplastic pancreatic controls (n = 5) by matrix-assisted laser desorption-ionization time-of-flight (MALDI-TOF) mass spectrometry. Both IPMN subgroups showed increased abundance of neutral composition H4N4 and decrease in H3N5F1, increase in sialylation, and decrease in sulfation, as compared to the controls. Furthermore, invasive IPMN showed an increase in terminal N-acetylhexosamine containing structure H4N5, and increase in acidic complex-type glycans, but decrease in their complex fucosylation and sulfation, as compared to the controls. In conclusion, the N-glycan profiles differed between healthy pancreatic tissue and non-invasive and invasive IPMNs. The unique glycans expressed in invasive IPMNs may offer interesting new options for diagnostics.

IgG glycomic profiling identifies potential biomarkers for diagnosis of echinococcosis

Echinococcosis caused by larval stage of the genus Echinococcus, is a serious and potentially fatal parasitic zoonosis distributed globally. The two types of the disease in human are cystic echinococcosis (CE) and alveolar echinococcosis (AE). As the biological and encysting characteristics of the parasite, early diagnosis remains to address. In the present study, we demonstrate the value of Immunoglobulin G (IgG) glycome as a potential diagnostic biomarker for echinococcosis. Serum IgG glycome profiles were analyzed by ultra-performance liquid chromatography in a cohort comprised of 127 echinococcosis patients, of them 98 were diagnosed as CE and 29 as AE. IgG N-glycome analysis in pretreatment serum of echinococcosis patients presents 25 glycans and 64 derived traits. Compared with IgG glycans of healthy control group, neutral glycans, fucosylation and agalactosylated N-glycans increased while sialylation and galactosylation decreased in echinococcosis patients. Combined with a machine-learning-based approach, we built three biomarker combinations to distinguish CE, AE and healthy controls. Meanwhile, galactosylation, sialylation and A2BG2S1 in IgG glycan profiles were evidently associated with different types of CE (from CE1 to CE5). Our findings suggest that the alterations in IgG N-glycome may be of value in CE and AE diagnosis and follow-up CE disease progress. The role of IgG N-glycans as diagnostic biomarker remains to be verified in future study.

IgG N-glycan Signatures as Potential Diagnostic and Prognostic Biomarkers

IgG N-glycans are an emerging source of disease-specific biomarkers. Over the last decade, the continued development of glycomic databases and the evolution of glyco-analytic methods have resulted in increased throughput, resolution, and sensitivity. IgG N-glycans promote adaptive immune responses through antibody-dependent cellular cytotoxicity (ADCC) and complement activation to combat infection or cancer and promote autoimmunity. In addition to the functional assays, researchers are examining the ability of protein-specific glycosylation to serve as biomarkers of disease. This literature review demonstrates that IgG N-glycans can discriminate between healthy controls, autoimmune disease, infectious disease, and cancer with high sensitivity. The literature also indicates that the IgG glycosylation patterns vary across disease state, thereby supporting their role as specific biomarkers. In addition, IgG N-glycans can be collected longitudinally from patients to track treatment responses or predict disease reoccurrence. This review focuses on IgG N-glycan profiles applied as diagnostics, cohort discriminators, and prognostics. Recent successes, remaining challenges, and upcoming approaches are critically discussed.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2019-2020

This review is the tenth update of the original article published in 1999 on the application of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2020. Also included are papers that describe methods appropriate to analysis by MALDI, such as sample preparation techniques, even though the ionization method is not MALDI. The review is basically divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of arrays. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other areas such as medicine, industrial processes and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. The reported work shows increasing use of incorporation of new techniques such as ion mobility and the enormous impact that MALDI imaging is having. MALDI, although invented nearly 40 years ago is still an ideal technique for carbohydrate analysis and advancements in the technique and range of applications show little sign of diminishing.

Glycosylation of immunoglobin G in tumors: Function, regulation and clinical implications

Immunoglobulin G (IgG) is the predominant component in humoral immunity and the major effector of neutralizing heterogeneous antigens. Glycosylation, as excessive posttranscriptional modification, can modulate IgG immune function. Glycosylated IgG has been reported to correlate with tumor progression, presenting several characteristic modifications, including the core fucose, galactose, sialic acid, and the bisect N-acetylglucosamine (GlcNAc). Meanwhile, IgG glycosylation regulates tumor immunity involved in tumor progression and is thus a potential target. Herein, we summarized the research progression to provide novel insight into the application of IgG glycosylation in tumor diagnosis and treatment.

Application of Mass Spectrometry in Pancreatic Cancer Translational Research

Pancreatic cancer (PC) is one of the most common malignant tumors in the digestive tract worldwide, with increased morbidity and mortality. In recent years, with the development of surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, and the change of the medical thinking model, remarkable progress has been made in researching comprehensive diagnosis and treatment of PC. However, the present situation of diagnostic and treatment of PC is still unsatisfactory. There is an urgent need for academia to fully integrate the basic research and clinical data from PC to form a research model conducive to clinical translation and promote the proper treatment of PC. This paper summarized the translation progress of mass spectrometry (MS) in the pathogenesis, diagnosis, prognosis, and PC treatment to promote the basic research results of PC into clinical diagnosis and treatment.

Diagnostic Potential of Plasma IgG N-glycans in Discriminating Thyroid Cancer from Benign Thyroid Nodules and Healthy Controls

Background

Novel biomarkers are urgently needed to distinguish between benign and malignant thyroid nodules and detect thyroid cancer in the early stage. The associations between serum IgG N-glycosylation and thyroid cancer risk have been revealed. We aimed to explore the potential of IgG N-glycan traits as biomarkers in the differential diagnosis of thyroid cancer.

Methods

Plasma IgG N-glycome analysis was applied to a discovery cohort followed by independent validation. IgG N-glycan profiles were obtained using a robust quantitative strategy based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. IgG N-glycans were relatively quantified, and classification performance was evaluated based on directly detected and derived glycan traits.

Results

Four directly detected glycans were significantly changed in thyroid cancer patients compared to that in non-cancer controls. Derived glycan traits and a classification glycol-panel were generated based on the directly detected glycan traits. In the discovery cohort, derived trait BN (bisecting type neutral N-glycans) and the glyco-panel showed potential in distinguishing between thyroid cancer and non-cancer controls with AUCs of 0.920 and 0.917, respectively. The diagnostic potential was further validated. Derived trait BN and the glycol-panel displayed “accurate” performance (AUC>0.8) in discriminating thyroid cancer from benign thyroid nodules and healthy controls in the validation cohort. Meanwhile, derived trait BN and the glycol-panel also showed diagnostic potential in detecting early-stage thyroid cancer.

Conclusions

IgG N-glycome analysis revealed N-glycomic differences that allow classification of thyroid cancer from non-cancer controls. Our results suggested that derived trait BN and the classification glyco-panel rather than single N-glycans may serve as candidate biomarkers for further validation.

Screening and diagnosis of colorectal cancer and advanced adenoma by Bionic Glycome method and machine learning

Colorectal cancer (CRC), one of the major health problems worldwide, mostly develops from colorectal adenomas. Advanced adenomas are generally considered as precancerous lesions and patients are recommended to remove the adenomas. Screening for colorectal cancer is usually performed by fecal tests (FOBT or FIT) and colonoscopy, however, their benefits are limited by uptake and adherence. Most CRC develops from colorectal advanced adenomas, but there is currently a lack of effective noninvasive screening method for advanced adenomas. N-glycans in human serum hold the great potentials as biomarker for diagnosis of human cancers. Our aim was to discover blood-based markers for screening and diagnosis of advanced adenomas and CRC, and to ascertain their efficiency in classifying healthy controls, patients with advanced adenomas and CRC by incorporating machine learning techniques with reliable and simple quantitative method with "Bionic Glycome" as internal standard based on the high-throughput Matrix-assisted Laser Desorption/Ionization Mass Spectrometry (MALDI-MS). The quantitative results showed that there is a positive correlation between multi-antennary, sialylated N-glycans and CRC progress, while bi-antennary core-fucosylated N-glycans are negatively correlated with CRC progress. Machine learning is a powerful classification tool, suitable for mining big data, especially the large amount of data generated by high-throughput technologies. Using the predictive model constructed by machine learning, we obtained the classification accuracy of 75% for classification of 189 samples including CRC, advanced adenomas and healthy controls, and the accuracy of 87% for detection of the disease group that required treatment, including CRC and advanced adenomas. To our delight, the model successfully applied to the prediction of 176 samples collected a few months later, and five samples were wrongly predicted in the disease group. Overall, this diagnostic model we constructed here has valuable potential in the clinical application of detecting advanced adenomas and colorectal cancer and could compensate for the limitations of the current screening methods for detection of CRC and advanced adenomas.

Identifying Novel Biomarkers Ready for Evaluation in Low-Prevalence Populations for the Early Detection of Upper Gastrointestinal Cancers: A Systematic Review

Introduction

Detecting upper gastrointestinal (GI) cancers in primary care is challenging, as cancer symptoms are common, often non-specific, and most patients presenting with these symptoms will not have cancer. Substantial investment has been made to develop biomarkers for cancer detection, but few have reached routine clinical practice. We aimed to identify novel biomarkers for upper GI cancers which have been sufficiently validated to be ready for evaluation in low-prevalence populations.

Methods

We systematically searched MEDLINE, Embase, Emcare, and Web of Science for studies published in English from January 2000 to October 2019 (PROSPERO registration CRD42020165005). Reference lists of included studies were assessed. Studies had to report on second measures of diagnostic performance (beyond discovery phase) for biomarkers (single or in panels) used to detect pancreatic, oesophageal, gastric, and biliary tract cancers. We included all designs and excluded studies with less than 50 cases/controls. Data were extracted on types of biomarkers, populations and outcomes. Heterogeneity prevented pooling of outcomes.

Results

We identified 149 eligible studies, involving 22,264 cancer cases and 49,474 controls. A total of 431 biomarkers were identified (183 microRNAs and other RNAs, 79 autoantibodies and other immunological markers, 119 other proteins, 36 metabolic markers, 6 circulating tumour DNA and 8 other). Over half (n = 231) were reported in pancreatic cancer studies. Only 35 biomarkers had been investigated in at least two studies, with reported outcomes for that individual marker for the same tumour type. Apolipoproteins (apoAII-AT and apoAII-ATQ), and pepsinogens (PGI and PGII) were the most promising biomarkers for pancreatic and gastric cancer, respectively.

Conclusion

Most novel biomarkers for the early detection of upper GI cancers are still at an early stage of matureness. Further evidence is needed on biomarker performance in low-prevalence populations, in addition to implementation and health economic studies, before extensive adoption into clinical practice can be recommended.

Electronic Supplementary Material

The online version of this article (10.1007/s12325-020-01571-z) contains supplementary material, which is available to authorized users.

Complementary Use of Carbohydrate Antigens Lewis a, Lewis b, and Sialyl-Lewis a (CA19.9 Epitope) in Gastrointestinal Cancers: Biological Rationale Towards A Personalized Clinical Application

Carbohydrate antigen 19.9 (CA19.9) is used as a tumor marker for clinical and research purposes assuming that it is abundantly produced by gastrointestinal cancer cells due to a cancer-associated aberrant glycosylation favoring its synthesis. Recent data has instead suggested a different picture, where immunodetection on tissue sections matches biochemical and molecular data. In addition to CA19.9, structurally related carbohydrate antigens Lewis a and Lewis b are, in fact, undetectable in colon cancer, due to the down-regulation of a galactosyltransferase necessary for their synthesis. In the pancreas, no differential expression of CA19.9 or cognate glycosyltransferases occurs in cancer. Ductal cells only express such Lewis antigens in a pattern affected by the relative levels of each glycosyltransferase, which are genetically and epigenetically determined. The elevation of circulating antigens seems to depend on the obstruction of neoplastic ducts and loss of polarity occurring in malignant ductal cells. Circulating Lewis a and Lewis b are indeed promising candidates for monitoring pancreatic cancer patients that are negative for CA19.9, but not for improving the low diagnostic performance of such an antigen. Insufficient biological data are available for gastric and bile duct cancer. Studying each patient in a personalized manner determining all Lewis antigens in the surgical specimens and in the blood, together with the status of the tissue-specific glycosylation machinery, promises fruitful advances in translational research and clinical practice.

Prediction of neoadjuvant chemotherapeutic efficacy in patients with locally advanced gastric cancer by serum IgG glycomics profiling

Background

Neoadjuvant chemotherapy (NACT) could improve prognosis and survival quality of patients with local advanced gastric cancer (LAGC) by providing an opportunity of radical operation for them. However, no effective method could predict the efficacy of NACT before surgery to avoid the potential toxicity, time-consuming and economic burden of ineffective chemotherapy. Some research has been investigated about the correlation between serum IgG glycosylation and gastric cancer, but the question of whether IgG glycome can reflect the tumor response to NACT is still unanswered.

Method

Serum IgG glycome profiles were analyzed by Ultra Performance Liquid Chromatography in a cohort comprised of 49 LAGC patients of which 25 were categorized as belonging to the NACT response group and 24 patients were assigned to the non-response group. A logistic regression model was constructed to predict the response rate incorporating clinical features and differential N-glycans, while the precision of model was assessed by receiver operating characteristic (ROC) analysis.

Results

IgG N-glycome analysis in pretreatment serum of LAGC patients comprises 24 directly detected glycans and 17 summarized traits. Compared with IgG glycans of non-response group, agalactosylated N-glycans increased while monosialylated N-glycans and digalactosylated N-glycans decreased in the response group. We constructed a model combining patients' age, histology, chemotherapy regimen, GP4(H3N4F1), GP6(H3N5F1), and GP18(H5N4F1S1), and ROC analysis showed this model has an accurate prediction of NACT response (AUC = 0.840) with the sensitivity of 64.00% and the specificity of 100%.

Conclusion

We here firstly present the profiling of IgG N-glycans in pretreatment serum of LAGC. The alterations in IgG N-glycome may be personalized biomarkers to predict the response to NACT in LAGC and help to illustrate the relationship between immunity and effect of NACT.